Research Methods Handout

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Introduction

Research is the cornerstone of any science, including both the hard sciences such as chemistry or physics and the social (or soft) sciences such as psychology, management, or education. It refers to the organized, structured, and purposeful attempt to gain knowledge about a suspected relationship. Many argue that the structured attempt at gaining knowledge dates back to Aristotle and his identification of of deductive reasoning. Deductive reasoning refers to a structured approach utilizing an accepted premise (known as a major premise), a related minor premise, and an obvious conclusion. This way of gaining knowledge has been called a syllogism, and by following downward from the general to the specific, knowledge can be gained about a particular relationship. An example of an Aristotelian syllogism might be: Major Premise:

All students attend school regularly

Minor Premise:

John is a student

Conclusion:

John attends school regularly

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In the early 1600s, Francis Bacon identified a different approach to gaining knowledge. Rather than moving from the general to the specific, Bacon looked at the gathering of specific information in order to make general conclusions. This type of of reasoning is called inductive and unlike Aristotelian logic allows new major premises to be determined. Inductive reasoning has been been adopted into the sciences as the preferred way to explore new relationships because it allows us to use accepted knowledge as a means to gain new knowledge. For example: Specific Premises:

John, Sally, Sally, Lenny and Sue attended class class regularly regularly

Specific Premises:

John, Sally, Sally, Lenny, and Sue received high grades grades

Conclusion:

Attending class regularly results in high grades

Researchers combine the powers of deductive and inductive reasoning into what is referred to now as the scientific method. It involves the determination of a major major premise (called a theory or a hypothesis) and then the analysis of the specific examples (r esearch) that would logically follow. The results might look something like: Major Premise:

Attending classes regularly results in high grades

Class Attendance:

Group 1:

John, Sally, Lenny and Sue attend classes regularly

Group 2:

Heather, Lucinda, Ling, and Bob do not attend classes regularly

Group 1:

John, Sally Lenny, and Sue received A’s and B’s

Group 2:

Heather, Lucinda, Ling, and Bob received C’s and D’s

(Suspected Cause)

Grades: (Suspected Effect) Conclusion:

Attending class regularly results in higher grades when compared with not attending class regularly (the Major Premise or Hypothesis is therefore supported)

Utilizing the scientific method for gaining new information and testing the validity of a major premise, John Dewey suggested a series of logical steps t o follow when attempting to support a theory or hypothesis with actual data. In other words, he proposed using deductive reasoning to develop a theory followed by inductive reasoning to support it.

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In the early 1600s, Francis Bacon identified a different approach to gaining knowledge. Rather than moving from the general to the specific, Bacon looked at the gathering of specific information in order to make general conclusions. This type of of reasoning is called inductive and unlike Aristotelian logic allows new major premises to be determined. Inductive reasoning has been been adopted into the sciences as the preferred way to explore new relationships because it allows us to use accepted knowledge as a means to gain new knowledge. For example: Specific Premises:

John, Sally, Sally, Lenny and Sue attended class class regularly regularly

Specific Premises:

John, Sally, Sally, Lenny, and Sue received high grades grades

Conclusion:

Attending class regularly results in high grades

Researchers combine the powers of deductive and inductive reasoning into what is referred to now as the scientific method. It involves the determination of a major major premise (called a theory or a hypothesis) and then the analysis of the specific examples (r esearch) that would logically follow. The results might look something like: Major Premise:

Attending classes regularly results in high grades

Class Attendance:

Group 1:

John, Sally, Lenny and Sue attend classes regularly

Group 2:

Heather, Lucinda, Ling, and Bob do not attend classes regularly

Group 1:

John, Sally Lenny, and Sue received A’s and B’s

Group 2:

Heather, Lucinda, Ling, and Bob received C’s and D’s

(Suspected Cause)

Grades: (Suspected Effect) Conclusion:

Attending class regularly results in higher grades when compared with not attending class regularly (the Major Premise or Hypothesis is therefore supported)

Utilizing the scientific method for gaining new information and testing the validity of a major premise, John Dewey suggested a series of logical steps t o follow when attempting to support a theory or hypothesis with actual data. In other words, he proposed using deductive reasoning to develop a theory followed by inductive reasoning to support it.

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Scientific Methods

There are many scientific methods. The two major methods are the inductive method and the deductive method. The deductive method involves the following three steps: 1. 2. 3.

State the hypothesis (based on theory or research literature). Collect data to test the hypothesis. Make decision to accept or reject the hypothesis.

The inductive method. This approach also involves three steps: 1. 2. 3.

Observe the world. Search for a pattern in what is observed. Make a generalization about what is occurring.

Virtually any application of science includes the use of both the deductive and the inductive approaches to the scientific method either in a single study or over time. The inductive method is as “bottom up” method that is especially useful for generating theories and hypotheses; while the deductive method is a “top down” method that is especially useful for testing theories and hypotheses.

What is RESEARCH? Research   comes from two words r e  and  comes  and search  which  which implies that research topics

are not new new or that such topics have not been discussed before. The present study serves only as a venue of confirmation, revision, or negation of the previous findings. Such results are still new which adds knowledge.   means s e a r c h i n g f o r a t h e o r y , t e s t i n g a t h e o r y , or s o l v i n g a Research  of rice in terms of yield per p r o b l e m . When our rice experts look for the best strain of hectare, palatability and sturdiness, we are looking for a theory. When we say that modular instruction is the best method of teaching and we would like to test this, actually we are testing a theory. In all these situations, where the  problem can not be solved  immediately, we need research. Research must give new knowledge, for what is known in the past may not be

applicable to what is contemporary. Past researches, however, constitute what is known as review of related literature. To define research formally, according to:

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CALMORIN (1994), “Research is a scientific investigation of phenomena which includes the collection, presentation, analysis, and interpretation of facts, that link man’s speculation with reality.” MARTINEZ (1988), “Research is a scientific process of critical selection of data, investigation and analysis of such to gain new knowledge or to complement an existing one.” KERLINGER (1973), “Research is a systematic, controlled, empirical, and critical investigation of hypothetical propositions about the presumed relations among natural phenomena.”

Choosing a Research Topic Choosing one's topic  is a very important step. If you have a choice of topics, you want to pick one that interests you and to which you have at least some access. Many researchers try to study topics that are fascinating, but cannot really be researched due to a variety of concerns. Sometimes travel expenses are too high, or the researcher cannot gain access. Patricia Adler's (1993) study of upper level drug dealers and smugglers, for example, could not be completed by just any researcher. Adler was somehow able to gain access to the dealers and their world. Likewise, researchers who want to know more about serial killers might have problems finding people to interview. Even "everyday" topics like why kids join gangs means the researcher may have to find some gang members to study. In other words, don't bite off more than you can chew.

The next problem is the breadth of the topic. You want to pick a topic that is "narrow" enough that your research is focused, but not so narrow that you can't find any infor mation on the topic. A researcher who is interested in Filipino indigents in criminal justice, for example, might easily find him/herself overwhelmed by the breadth of the topic. If the topic is narrowed to Filipino judges, there will be fewer problems, but the topic is still rather broad. A better choice would be factors that affect the sentencing of misdemeanants by Filipino judges. An example of a "too narrow" topic might be child custody decisions by lesbian judges. No doubt it's an interesting topic, but it probably doesn't have enough information for a student to do a research study on it. EXERCISE: Which of the following would be the best topic for a graduate student working on a one-term class project?  A. teenagers' attitudes B. organized crime figure's beliefs about how to reduce poverty in city areas C. the factors that students feel were important in their decisions to attend your college/university D. how the Philippine courts operate

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What are the different sources of Research Problem?

The following are the possible sources of research problem: 1. Experiences 2. Problems in the work environment 3. Classroom discussions 4. Technological and scientific advancements 5. Offshoots of other researches 6. Suggestions from friends and administrators

THE RESEARCH PROCESS

Man’s faculties enable him to speculate and theorize about reality. However, discrepancy exists in various degrees between what he thinks is happening and what is really happening. What he thinks happens is the theoretical domain,  whereas what really happens is the experiential dimension . What research does is to bridge the gap between these two domain polarities. The Theoretical Pole  consists largely of the researcher’s speculation regarding reality. The Empirical Pole   contains the basic elements of what actually is happening in reality, in social groups and within the individual. The Research Process  links the two poles together. Below is a diagram that demonstrates how the research process links the theoretical to the empirical pole.

Theoretical Pole Basic Elements 1. Concepts 2. Propositions 3. Logical Relations

Research Process Hypotheses Basic Elements 1. Research Design 2. Sampling Plan  Analyzed 3. Measurement 4. Data Analysis Data

Empirical Pole Operational Hypotheses *Reality *Social(groups and societies) *Psychological Collected (Individual) Data

Figure 1. Components of the Research Process

The diagram indicates that the theoretical pole  contains the basic elements of concepts, propositions and logical relation. Concept is a term and its corresponding definition. A proposition is a statement specifying the relationship between two or more variables. However, it may not specify how the two variables are connected. Logical relations attempt to establish the relationship between two variables.

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Research serves as the “workhorse” for linking the two poles together. The theoretical pole and research methods are linked together by the hypotheses. On the other hand, the research methods and the empirical pole are linked together by the operational hypotheses. Operational hypotheses are a restatement of the original hypotheses in terms of the specific research design, sampling plan and measurement procedures. After data is collected the empirical pole is linked with research methods.  And when collected data is analyzed, research methods are also linked with the theoretical pole. One can see the advantages that can be derived by putting these elements into operation. First, hypotheses can be generated from the theoretical pole, thus guiding the research project to completion. Second, the other data collected through application of data analysis, procedures and tools can generate additional concepts and hypotheses.

Therefore, research is a continuous movement back and forth between these two poles, a link between man’s speculation and reality.

STEPS IN THE CONDUCT OF RESEARCH

The following are the general steps in the conduct of research:

Figure 2. Steps in Research FIVE BIG WORDS IN RESEARCH

Here, I want to introduce you to five terms that I think help to describe some of the key aspects of research.

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1. Theoretical. This means that much of it is concerned with developing, exploring or testing the theories or ideas that social and educational researchers have about how the world operates. 2. Empirical. Although, sometimes contrasted with theoretical. This means that research is based on observations and measurements of reality  –  on what we perceive of the world around us. 3. Nomothetic. This refers to laws or rules that pertain to the general case ( nomos in Greek) and is contrasted with the term “Idiographic” which refers to laws or rules that relate to individuals. In any even the point here is, research is concerned with the nomothetic  –  the general case  –  rather than the individual. We often study individuals, but usually we are interested in generalizing to more than just the individual. 4. Probabilistic. This means that research is based on probabilities. The inferences that we make in research have probabilities associated with them  –  they are seldom meant to be considered covering laws that pertain to all cases. Part of the reason we have seen statistics become dominant in research is that it allows us to estimate probabilities for the situations we study. 5. Causal. (NOTE: You’ve got to be very careful with this term. It is spelled causal not casua)l. This means that most research is interested (at some point) in looking at cause-effect relationships. This does not mean that most studies that simply observe – for instance, surveys that seek to describe the percent of people holdin g a particular opinion. And there are many studies that explore relationships  – for example, studies that attempt to see whether there is relationship between gender and salary.

VARIABLES

Statistical thinking starts with an awareness and understanding that no two things are alike and that variability is inherent to all things (Levine, et.al. 1995). One may wonder why scores are variables; why job performance vary from individual to individual; or why research productivity varies from teacher to teacher. The study of variables is therefore the focus of statistics and research. Falik and Brown (1983) defines a variable    as any characteristic of objects or individuals that can vary either in quality or quantity. Thus, for instance, characteristics of individuals such as I.Q., gender, height, weight, political affiliation, religion, etc. are examples of variables. In general, variables that can vary in quantity are called Quantit ative Variables while those that can vary in quality are called Qualitative Variables .  A co n s t a n t   is any characteristic of objects that cannot vary. For instance, skin color is a variable if we talk about a class in research composed of international students. If the students in this class are typical residents of Calbayog City, then skin variable can not vary and hence it becomes a constant.

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In research, it is important to know the nature of the variables included in a particular study. In general, variables maybe classified according to three criteria, namely: according to f u n c t i o n a l r el a t io n s h i p   , according to c o n t i n u i t y o f s c a l e s ,  and according to l e v el o f m e a s u r e m e n t .  VARIABLES CLASSIFIED ACCORDING TO FUNCTIONAL RELATIONSHIP In a study involving several variables, it is important to determine the nature of relationships between or among these variables. A variable that is influenced or affected by another variable is called a d e p e n d e n t v a r i ab l e  . The variable that influences the variation of another variable is called an i n d e p e n d e n t v a r i ab l e .  D e p e n d e n t and i n d e p e n d e n t v a r i ab l e    are distinguished by their t e m p o r a l sequence. In terms of cause and effect relationship, the independent variable  is the

cause while the dependent variable is the effect . Hence, the independent variable comes first before any effect on the dependent variable can be ascertained.

Other classification of variable according to functional relationship are m o d e r a t o r variable, con trol variable, and n u i s a n c e v a r i ab l e .  These terms are used to describe an independent variable whose influence on the dependent variable is viewed in relation to another independent variable (moderator) or is kept constant (controlled) or is beyond the control of the researcher (nuisance). VARIABLES CLASSIFIED ACCORDING TO CONTINUITY OF SCALE Quantitative Variables   can be discrete or continuous. Quantitative variables that can only assume a value of a whole number are called discrete variables. These variables usually result from a process of counting and are

restricted to whole numbers or integers. On the other hand, quantitative variables that can take on any real value in a specified set of values are called c o n t i n u o u s v a r i a b l es   . These variables represent numerical measurements on a continuous dimension or scale and can take any numerical value within a continuum or interval. The number of children in a household is an example of a disc rete variable  . This variable can not assume a value of ½ or 0.9. The only possible values of this variables are 0, 1, 2, 3, 4, etc. which are obviously whole numbers. The height of school children, on the other hand, is a continuous (not a discrete!) variable since from a specified range of heights of the children, this variable can take on infinitely many values within the specified range of heights of school children. Other examples of continuous variables are length, weight, age, or the anxiety level of students in research.

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VARIABLES CLASSIFIED ACCORDING TO LEVEL OF MEASUREMENT Measurement is the assignment of numbers or codes to observations. Levels of measurement are distinguished by ordering and distance properties. The traditional classification of l e v el s o f m e as u r e m e n t    into n o m i n a l , o r d i n a l   , interval  , and ratio   scales was developed by S.S. Stevens (1946). This remains the basic typology or classification. NOMINAL MEASUREMENT. The N o m i n a l M ea s u r e m e n t  is the “lowest” in the typology

because no assumptions are made about relations between values. Each value defines a distinct category and serves merely as a label or name (hence, “nominal” level) for the category. For instance, the birthplace of an individual is a nominal variable. For most purposes, there is no inherent ordering among cities or towns. Although cities can be ordered according to size, density, or air pollution, a city thought of as “place of birth” is a concept that is normally not tied to any order. When numeric values are attached to nominal categories, they are merely identifiers. None of the properties of numbers such as relative size, addition or multiplication, can be applied to these numerically coded categories. Therefore, statistics that assume ordering or meaningful distances between the values do not ordinarily give meaningful information about nominal variables. ORDINAL MEASUREMENT. When it is possible to rank or order categories according to some criterion, the o r d i n a l l e v e l o f m e a s u r e m e n t   is achieved. For instance,

classifying employees into clerical, supervisory, and managerial categories is an ordering according to responsibilities or skills. Each category has a position lower or higher than another category. Furthermore, knowing that supervisory is higher than clerical and that managerial is higher than supervisory automatically means managerial is higher than clerical. However, nothing is known about how much higher; no distance is measured. Ordering is the sole mathematical property applicable to ordinal measurements, and the use of numeric values does not imply that any other property of numbers is applicable. . In addition to order, i n t e r v al m e as u r e m e n t s     have the INTERVAL MEA SUREMENT  property of meaningful distance between values. A thermometer, for example, measures temperature in degrees which are the same size at any point on the scale. The difference between 20oC and 21 oC is the same as the difference in 5 oC and 6oC. However, an interval scale does not have an inherently determined zero point. In the familiar Celsius and Fahrenheit systems, 0 o  is determined by the agreed-upon definition, not by the absence of heat. Consequently, interval-level measurement    allows us to study differences between items but not their proportionate magnitudes. For example, it is incorrect to say that 80 oF is twice as heat as 40 oF. . RATIO MEASUREMENT 

Ratio measurements have all the ordering and distance properties of an interval scale. In addition, a zero point can be meaningfully designated. In measuring physical distances between objects using feet or meters, a zero distance is naturally defined as the absence of any distance. The existence of a zero point means that ratio comparisons can be made. For example, it is quite meaningful to say that a 6foot-tall adult is twice as tall as a 3-foot-tall child or that a 500-meter race is five times as long as a 100-meter race.

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Because ratio measurements satisfy all the properties of a real number system, mathematical manipulations appropriate for real numbers can be applied to ratio measures. However, the existence of a zero point is seldom critical for statistical analyses.

EVALUATING THE RESEARCH PROBLEM

Before the proposed research problem can be considered appropriate, several searching questions should be raised. Only when these questions are answered in the affirmative can the problem be considered a good one: 1. Is t h i s t h e t y p e o f p r o b l e m t h a t c a n b e ef f e c t i v el y s o l v e d t h r o u g h t h e p r o c e s s of research ?  Can relevant data be gathered to test the theory or find the answer to the question under consideration? 2. Is t h e p r o b l e m s i g n i f i c an t ?    Is an important principle involved? Would the solution make any difference as far as educational theory or practice is concerned? If not, there are undoubtedly more significant problems waiting to be investigated. 3. Is t h e p r o b l e m a n e w o n e ?   Is the answer already available? Ignorance of prior studies may lead a student to spend time needlessly on a problem already investigated by some other worker. However, although novelty or originality is an important consideration, simply because a problem has been investigated in the past does not mean that it is no longer worthy of study. At times it is appropriate to replicate (repeat) a study to verify its conclusions or to extend the validity of its findings to a different population or situation. 4. Is r e s e a r c h o n t h e p r o b l e m f e as i b l e ?   After a research project has been evaluated, there remains the problem of suitability for a particular researcher. The student should ask: although the problem may be a good one, is it a good problem for me? Will I be able to carry it through to a successful conclusion? Some of the questions the students should consider are the following: a. Am I competent  to plan and carry out a study of this type? Do I know enough about this field to understand its significant aspects and to interpret my findings? Am I skillful enough to develop, administer, and interpret the necessary data-gathering devices and procedures? Am I well grounded in the necessary knowledge of research design and statistical procedures? b. Are pertinent data accessible? Are valid and reliable data-gathering devices and procedures available? Will school authorities permit me to contact the students, conduct necessary experiments or administer necessary tests, interview teachers, or have access to important cumulative records? Will I be able to get the sponsorship necessary to open doors that otherwise would be closed to me?

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c. Will I have the necessary financial resources to carry on this study? What will be the expense involved in data-gathering equipment, printing, test materials, travel, and clerical help? If the project is an expensive one, what is the possibility of getting a grant from a philanthropic foundation or from governmental agencies? d. Will I have enough time  to complete the project? Will there be time to devise the procedures, select the data-gathering devices, gather and analyze the data, and complete the research report? Because most academic programs impose time limitations, certain worthwhile projects of a longitudinal type are precluded. e. Will I have the determination  to pursue the study despite the difficulties and social hazards that may be involved? Will I be willing to work aggressively when data are difficult to gather and when others are reluctant to cooperate? Controversial problem areas such as sex education and multiculturalism are probably not appropriate for a beginning research project. 5. Is the research qu estion c lear?  Since the research question is the focus of a research investigation, it is particularly important that the question is clear. What exactly is being investigated? Let us consider two examples of questions that are not clear enough. First , “Is humanistically oriented classroom effective?” Although the phrase “ humanistically oriented classroom” may seem quite clear, many people may not be sure exactly what it means. Another term which is ambiguous is the term “effective”. Does it mean “results in increased academic proficiency?”, “results in happier children”, or “costs less money”? Maybe it means all these things! Second, “How do teachers feel about special classes for the educationally handicapped?” The first term that needs clarification is “teachers.” What age group does this involve? Level of experience?, etc. Likewise the phrase “feel about” is also ambiguous. Does it mean op inions? Emotional reactions? Or what? The terms “special classes” and “educationally handicapped” also need to be clarified.

Defining terms operationally is a helpful way to clarify the meaning of the ambiguous terms. 6. W i l l t h e i n v e s t i g a t i o n b e e t h i c a l ? observe the following things: a. b. c. d.

voluntary participation informed consent risk of harm confidentiality

Research to be ethical must consider or

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e. anonymity f. right to service

NwSSU RESEARCH MANUSCRIPT FORMAT

Title Page  Approval Sheet  Acknowledgment Dedication Table of Contents List of Tables List of Figures List of Appendices Thesis Abstract CHAPTER 1 The Problem and Its Setting   Introduction  Theoretical Framework  Conceptual Framework  Statement of the Problem  Null Hypotheses  Significance of the Study  Scope and Limitations of the Study  Definition of Terms CHAPTER 2 Related Literature and Studies  Related Literature  Related Studies CHAPTER 3 Methodology  Research Design  Locale and Time of the Study  Respondents/Subjects of the Study  Sampling Technique   Instrumentation  Validation Procedure  Data Gathering Procedure  Statistical Treatment CHAPTER 4 Presentation, Analysis, and Interpretation of Data CHAPTER 5 Summary of Findings, Conclusions, and Recommendations   Findings   Conclusions   Recommendations Bibliography  Appendices Curriculum Vitae/About the Author

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WRITING THE TITLE OF THE INVESTIGATION  

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This should serve as the frame of reference for the entire study (Adanza, 1995). This must be brief, specific, and relevant to the researcher, to his work, and the community (Vizcarra, 2003). Included in the title are words or phrases under which, in an index, a scholar would search for a paper containing the particular content included in the report (De Belen, 1984). It answers the “what”, “who”, and “where”. The “ when” is NOT included because it can be treated in “Scope and Delimitation” part of the study. Avoid the use of articles a, an, and the as a beginning word in the title.  Avoid using the terms “An Analysis of …”, “A Study of …,” An Investigation of …,” “An Evaluation of …”, “An Assessment of …”, and the like. All these things are understood to have been done or to be done when a research is conducted (De Belen, 1984; Calderon and Gonzales,2004). These words just lengthen the title unnecessarily without adding much meaning to it. It must be “eye-catching” and “thought -provoking” in order to:  Catch the readers’ attention.   Arouse the readers’ intellectual curiosity into reading further the text of the manuscript.  Achieve brevity. If the title contains more than one line, it must be written like an inverted pyramid. All words in capital letters (Calderon and Gonzales, 2004). Humorous or catchy titles are not appropriate for research papers (De Belen, 1984). The research title should demarcate an indication of the ENVISAGED SOLUTION or possible NEW PRODUCT. The title must contain the subject matter of the study, the locale of the study, the population involved, and the period when the data were gathered or will be gathered. For example,

THE TEACHING OF SCIENCE IN THE HIGH SCHOOLS OF CALBAYOG CITY AS PERCEIVED BY THE SCIENCE TEACHERS AND STUDENTS DURING THE SCHOOL YEAR 2007-2008 The contents as required by the guideline are: Subject Matter  Locale of the Study Population involved  Period of the Study 

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The Teaching of Science High Schools of Calbayog City The Science Teachers and Students School Year 2007 – 2008

However, by observing all the other guidelines in title formulation we can write a brief and concise form of the given title and a better one as follows:

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TEACHING OF SCIENCE IN THE HIGH SCHOOLS OF CALBAYOG CITY It will be noted that the article THE, the population, the science teachers and students as well as the period of the study, 2007-2008, are omitted when writing the concise form but they have to be mentioned in the scope and limitations of the study.

WRITING THE INTRODUCTION  

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This explains the background of the study (Castillo, 2001). This provides information on the setting like the geographical location, cultural and demographic characteristics, political or economic information, etc. The introductory statement must be ‘eye -catching’. Discussion in this section is mostly deductive in approach (Macro to micro). It starts with a very general view of the background of the problem and ending up in a localized setting. This provides a thorough justification on the choice of the research problem by citing situations or statements of authorities to explain why the study is being conducted. Statistics that may help to justify and concretize the existence of the problem should form a part of the discussion. The last part of the discussion is a brief situational analysis to present information on the specific problem and what prompts the researcher to venture into such a study (Vizcarra, 2003). HOW TO START:  Start with a brief but provocative quotation that is applicable to the theme of the study.  Use striking facts or statistics that portray objectively the problem situation.  Present a very brief background or resume of events depicting truly the present state of the issue abroad, in the country, and in the locality.  Work on the researcher’s firm stand on the need to bridge the gap between existing bodies of knowledge and the prevailing problem situation.  Develop the researcher’s  rationale concerning the need to replicate a completed study.  Challenge some pertinent universally held theories which is being envisioned for the study:

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Start with a broad general statements and narrows down progressively to a  point where it is finally linked to the rationale of the research project

Figure 3. The Inverted-Pyramid Approach in Writing Introduction

THE THEORETICAL FRAMEWORK  

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This portion justifies the rationale for the investigation (Castillo, 2001). Theory/ies on which the study is premised should be cited in order to establish the relationship among the variable/s in the study. Thorough documentation on the sources of the theory/ies cited should be made. For example, The researcher is working on a thesis entitled “Determinants of J ob Satisfaction and Productivity of Secondary School Teachers in Cal bayog City.” 

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The variables in the study are “Job Satisfaction” and “Productivity”, underscoring Maslow’s Theory of Motivation and McClelland’s Theory, some of the theories on motivation which discuss the level of motivation influencing job satisfaction and productivity. Theory Construction – How to? Step 1. Facts observed Step 2. Evolvement of the researcher’s own theory Step 3. Citing well-known authority – sources to support the resea rcher’s evolved theory.

THE CONCEPTUAL FRAMEWORK 

This refers to the researcher’s concepts or ideas in carrying out the study as it relates to the theoretical framework. Various consideration in the formulation of the researcher’s own concept is placed on the localized situation. That is,

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making the theoretical framework more applicable to local situation and workable. A conceptual paradigm is made usually consisting of inputs, process, and outputs (Castillo, 2001). This paradigm should be explained in detail by the researcher. This should serve as an inductive approach towards the specific statement of the problem in the study.

THE STATEMENT OF THE PROBLEM 

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This sets the direction of the inquiry or the verbalization of the “question” which the study proposes to answer (Castillo, 2001). This is divided into two (2) major parts: The General Problem Statement and the Specific Problem Statement. The general problem statement is the whole focus, which is reflected in the title of the study. It can be stated by briefly pointing out objectives, the subject, and the coverage as well as the time frame (Vizcarra, 2003). The specific problem statements maybe stated declaratively or interrogatively. These are the subdivisions or the breakdown of the main variable into its component. The pattern of stating or asking question should be based upon the three (3) levels of inquiry as suggested by Dickoff (as quoted by Adanza, 1995).

Three Levels of Inquiry Level 1: These are questions asked when the researcher has limited knowledge of the topic. They usually start with “what” and are exploratory in nature. They are prominent in descriptive researches. Illustration: 1. What is the profile of the respondents in terms of: 1.1 age, 1.2 sex, and 1.3 educational attainment? Level 2: These are questions on relationships or effects of variables. Illustration: 2. Is there a significant relationship between home environment and academic performance of Fourth Year High School students in the SlS in TTMIST, Calbayog City? Level 3: These are questions which assume relationships and effects and ask “why” of the results. This type of questions involves more variables, outcomes and predictions.

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Illustration: 3. To what extent do mental ability and home environment factors influence the learning of students in Topology?

4. How effective is the prediction on grades in English 1 when information on hours of study habits and mental ability are known?

Stating the Problems of the Study The statement of the problem may be stated in question form or topical form:

QUESTION FORM Specifically, the study seeks to answer the following questions: 1. What are the leadership skills of the managers of the ABC Corporation in terms of: 1.1 human relations 1.2 technical 1.3 administrative, and 1.4 institutional skills as perceived by themselves and their subordinates? 2. Is there a significant difference in the perceptions of the two groups of respondents on the leadership skills of managers, in terms of: 2.1 human relations 2.2 technical 2.3 administrative, and 2.4 institutional skills?

TOPICAL FORM The study seeks to determine the following: 2. The leadership skills of the managers of the ABC Corporation in terms of: 1.4 human relations 1.5 technical 1.6 administrative, and 1.4 institutional skills as perceived by themselves and their subordinates. 2. The significant difference of the perceptions of the two groups of respondents on the leadership skills of managers, in terms of: 2.5 human relations 2.6 technical 2.7 administrative, and 2.8 institutional skills.

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THE NULL HYPOTHESIS 

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These are statements of “no significant relationship/difference” which is mainly a negation of the second level specific problem statements. The order or arrangements of the statements follow from the second level specific problem statements.

The hypothesis is directly related to a theory but contains operationally defined variables and is in testable form. Hypotheses allow us to determine, through research, if our theory is correct. For example, “does prior work experience result s in better grades?” When doing research, we are typically looking for some type of difference or change between two or more groups. For example, we wanted to test the difference between “having work experience” and “not having work experience on college grades”. Every study has two hypotheses; one stated as a difference between groups  and one stated as no difference between groups. When stated as a difference between groups, our hypothesis would be, “stud ents with prior work experience earn higher grades than students without prior work experience.” This is called our research or scientific hypothesis. Because most statistics test for no difference, however, we must also have a null hypothesis. The null hypothesis is always written with the assumption that the groups do not differ. For example, the null hypothesis would state that, “students with work experience will not receive different grades than students with no work experience.” The null hypothesis is what we test through the use of statistics and is abbreviated Ho. Since we are testing the null, we can assume then that if the null is not true then some alternative to the null must be true. The research hypothesis stated earlier becomes our alternative, abbreviated H1. In order to make research as specific as possible we typically look for one of two outcomes, either the null or the alternative hypothesis. To conclude that there is no difference between the two groups means we are accepting our null hypothesis. If we, however, show that the null is not true then we must reject it and therefore conclude that the alternative hypothesis must be true. While there may be a lot of gray area in the research itself, the results must always be stated in black and white.

SIGNIFICANCE OF THE STUDY

The following points are important to note in the significance of the study: This notes the contribution of the proposed study either to a body of scientific knowledge, to practitioners in the area of research, or to any other group who will benefit from the results of the study. 

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This is started with an introductory statement implying an enumeration of the individuals or groups who served as the beneficiary of the results of the investigation. The beneficiaries should be arranged according to importance and the extent of the benefits they will get, that is macro to micro approach, or from general to specific approach is ideal, vis-à-vis the statements specifically indicating the benefits each could be derived.

SCOPE AND LIMITATIONS OF THE STUDY

Some points to remember in this section are the following: Included in this portion are the boundaries like geographic, population, time, and variables to be discussed (Castillo, 2001). Limitations of the study emanating from certain weaknesses/shortcomings should be noted in this section. Reasons for excluding them in the investigation should be discussed. 



THE DEFINITIONS OF TERMS

Researchers should remember the following points in formulating the section on definition of terms: This section provides a definition for the terms repeatedly used throughout the discussion which is usually the variables of the study. The definition usually starts with conceptual definition  and followed by the operational definition . Conceptual definition is mostly concerned with attributing authorities like books, magazines, etc., including unpublished materials (Vizcarra, 2003). Operational definition of terms is done when a researcher defines the terms as he uses them in the study (Vizcarra, 2003). They can be defined according to the measurements of variables. Technical studies usually define terms as an explanatory device (Vizcarra, 2003). 





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Illustration: Variable

Conceptual Definition

Operational Definition

Work Effort

Speed

My job requires me to work fast for ___ hours per day (1-2, 3-5, 6+)

Hardness

My job requires me to work hard for ___ hours per day (1-2, 3-5, 6+)

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Effort

My job requires a lot of effort for ___ hours per day (1-2, 3-5, 6+)

Dexterity

My job requires a lot of dexterity for ___ hours per day (1-2, 35, 6+)

Repetitiveness

My job is doing repetitive things for ___ hours per day (1-2, 3-5, 6+)

RELATED LITERATURE

Points to remember: This is an exhaustive, comprehensive, and selective discussion of the theories not included in the theoretical framework, but which have relation to the proposed study on the problem dimension. The theories discussed in this section should come from books, documents, articles, etc. which are closely related to the present study (Castillo, 2001). Literatures cited in this section should be properly documented. The discussion is organized logically usually making use of the order of the specific problem statement as basis for organization. At some point, organization maybe is according to the arrangement of the variables. The discussion should include the links, the similarities, and the dissimilarities among the works cited and the proposed research. 

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RELATED STUDIES

Points to remember: This is an exhaustive, comprehensive, and selective discussion of the ideas from previous researches which have relation to the proposed study on the problem dimension. The ideas discussed in this section should come from theses/dissertations, research journals, etc. which are closely related to the present study (Castillo, 2001). Studies cited in this section should be properly documented. The discussion is organized logically usually making use of the order of the specific problem statement as basis for organization. The discussion should include the links, the similarities, and the dissimilarities among the studies cited and the proposed research. 

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 

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SYNTHESIS 

These are concluding statements which provide a synthesis of the results of the review of literature and studies.

THE RESEARCH DESIGN  

This deals with the research design and the technique to be used in the study. It also include an overview of how the respondents/subjects will be chosen, how the rational size will be determined, the instruments to be used and their validation, and the data analyses scheme which include the application of statistical tools for treatment of data yielded by the study.

There are two types of study designs, experimental and quasi-experimental. Experimental: The experimental design uses a control group and applies treatment to

a second group. It provides the strongest evidence of causation through extensive controls and random assignment to remove other differences between groups. Using the evaluation of a job training program as an example, one could carefully select and randomly assign two groups of unemployed welfare recipients. One group would be provided job training and the other would not. If the two groups are similar in all other relevant characteristics, you could assume any differences between the groups employment one year later was caused by job training. Whenever you use an experimental design, both the internal and external validity can become very important factors. Internal validity  : The extent to which accurate and unbiased association between the IV and DVs were obtained in the study group. External validity : The extent to which the association between the IV and DV is accurate and unbiased in populations outside the study group.

(Please see a separate Handout for EXPERIMENTAL RESEARCH DESIGNS  – Appendix  A) The quasi-experimental design does not have the controls employed in an experimental design (most social science research). Although internal validity is lower than can be obtained with an experimental design, external validity is generally better and a well designed study should allow for the use of statistical controls to compensate for extraneous variables. Quasi-experimental:

Types of quasi-experimental design:

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1. Cross-sectional study : obtained at one point in time (most surveys) 2. Case study : in-depth analysis of one entity, object, or event 3. Panel study  : (cohort study) repeated cross-sectional studies over time with the same participants 4. Trend study : tracking indicator variables over a period of time (unemployment, crime, dropout rates) (Please see a separate Handout for OTHER RESEARCH DESIGNS) Common Types of Research Designs Three commonly used research types or designs are quantitative, qualitative, and mixed research. 

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Quantitative research follows a deductive research process and involves the collection and analysis of quantitative (i.e., numerical) data to identify statistical relations of variables. Common quantitative research methods include: content (relational) analysis, experiments, observations (scaled ratings, checklists), and surveys (closed-ended, validated scales) Qualitative research follows an inductive research process and involves the collection and analysis of qualitative (i.e., non-numerical) data to search for patterns, themes, and holistic features. Common qualitative research methods include: content (conceptual) analysis, focus groups, observations (narrative, comments), interviews, and surveys (open-ended). Mixed research combines or mixes quantitative and qualitative research techniques in a single study. Two sub-types of mixed research includes mixed method research—using qualitative and quantitative approaches for different phases of the study—and mixed model research—using quantitative and qualitative approaches within or across phases of the study.

Research approaches are generally categorized into quantitative and qualitative design. Each research design may be further classified into any of the four different research purposes: to explore  (an attempt to generate ideas about educational phenomenon), describe  (an attempt to describe the characteristics of educational phenomenon), predict (an attempt to forecast an educational phenomenon), and explain (an attempt to show why and how an educational phenomenon operates). Because both quantitative and qualitative approaches have weaknesses that limit the research purposes for which they are appropriate, a mixed research approach may be used that takes advantage of the complementary strengths of the qualitative and quantitative approaches.

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Research purpose

Preferred approach Qualitative

Quantitative

Mixed

Explore

X

X

Describe

X

X

Predict

X

X

Explain

X

X

TIME AND LOCALE OF THE STUDY 

This is a discussion about the time when the study conducted and the setting or place

THE SUBJECTS/RESPONDENTS OF THE STUDY 

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This section describes the population, why and how the respondents/subjects are to be chosen. The sampling technique may also be discussed in passing.

SAMPLING TECHNIQUE 

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It begins with the definition of the population. To define the population is to indicate the characteristics of the elements from which the sample will be taken (Castillo, 2001). A careful explanation of how to choose the units that will provide the data should follow. Indicate the sample size and the population frame from which the samples well be taken.

(Please see a separate Handout for SAMPLING)

INSTRUMENTATION 

This contains a description of the instrument/s used in the investigation and a discussion of how to use the same instrument. Explanation of the developmental processes involved in the creation of the instrument should form a part of the discussion.

(Please see a separate handout for RESEARCH INSTRUMENTS)

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VALIDATION OF THE INSTRUMENT 

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This includes a discussion on the validation aspects employed when the instrument was developed, the sample used in the try out, as well as the place where the validation where conducted. The reliability estimate used should also be discussed and the resulting reliability coefficient should be described.

THE GATA GATHERING PROCEDURE 

A discussion on the steps undertaken in gathering the needed data from seeking permission for the fielding of the instrument up to the process of retrieval of the data. This should be explained in detail, step by step that will lead the readers to follow through the process engaged in by the researcher.

Tabulated Example

ENHANCING SCHOOL LEADING PERFORMANCE OF EDUCATIONAL ADMINISTRATORS IN PHILIPPINE PUBLIC ELEMENTARY SCHOOLS Summary Table of Data Requirements and Data Gathering Techniques Research objective 1. Develop researchbased criteria of effective performance

Research activity -Identification and description of actual tasks and activities of school leaders (principals, head teachers, and teacher-incharge) -Identification of determinants of effective performance -Formulation of a model of performance -Validation of the

Source of information/ Key information - School leaders - Division and district - DECS officials - Performance rating and reports - DECS memoranda - Previous researches and studies - Records of meetings, seminars,

Methodology

Result/ Output

- Literature review - Interviews - FGD - Participants

- Tasks and activities identified and calcified into specific areas of

observation - Document analysis

responsibilities - Determinants of effective school leader - Performance model for school

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2. Assess the managerial capabilities of selected groups of school leaders

model -Development of instruments

workshops, etc.

- Assessment of practices against the validated model - Development of competency profile of school leaders

- Results derived from the above mentioned activities

- Interviews - FGD - Participants observation -Field survey - Statistical tests

leaders formulated - Model tested and validated - Necessary instruments prepared - Observations on tasks understanding of school leaders - Observations on school leader performance

3. Identify training needs requirements of school leaders

- Analysis of training needs

4. Recommend strategies and guidelines for training of school leaders

- Identify possible training options and courses for school leaders

- Results derived from the above mentioned activities

- Interviews - FGD -Participant observation - Field survey - Statistical tests

- Results derived from the above mentioned activities

- Interviews - FGD

- Training needs requirements of school leaders identified - Strategies and guidelines for INSET of school managers

Unobtrusive Measures Unobtrusive measures are measures that don't require the researcher to intrude in the research context. Direct and participant observation require that the researcher be physically present. This can lead the respondents to alter their behavior in order to look

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good in the eyes of the researcher. A questionnaire is an interruption in the natural stream of behavior. respondents can get tired of filling out a survey or resentful of the questions asked. Unobtrusive measurement presumably reduces the biases that result from the intrusion of the researcher or measurement instrument. However, unobtrusive measures reduce the degree the researcher has over the type of data collected. For some constructs there may simply not be any available unobtrusive measures. Three types of unobtrusive measurement are discussed here. 1. Indirect Measures  An indirect measure is an unobtrusive measure that occurs naturally in a research context. The researcher is able to collect the data without introducing any formal measurement procedure. The types of indirect measures that may be available are limited only by the researcher's imagination and inventiveness. For instance, let's say you would like to measure the popularity of various exhibits in a museum. It may be possible to set up some type of mechanical measurement system that is invisible to the museum patrons. In one study, the system was simple. The museum installed new floor tiles in front of each exhibit they wanted a measurement on and, after a period of time, measured the wear-and-tear of the tiles as an indirect measure of patron traffic and interest. We might be able to improve on this approach considerably using electronic measures. We could, for instance, construct an electrical device that senses movement in front of an exhibit. Or we could place hidden cameras and code patron interest based on videotaped evidence. One of my favorite indirect measures occurred in a study of radio station listening preferences. Rather than conducting an obtrusive survey or interview about favorite radio stations, the researchers went to local auto dealers and garages and checked all cars that were being serviced to see what station the radio was currently tuned to. In a similar manner, if you want to know magazine preferences, you might rummage through the trash of your sample or even stage a door-to-door magazine recycling effort. These examples illustrate one of the most important points about indirect measures -you have to be very careful about the ethics of this type of measurement. In an indirect measure you are, by definition, collecting information without the respondent's knowledge. In doing so, you may be violating their right to privacy and you are certainly not using informed consent. Of course, some types of information may be public and therefore not involve an invasion of privacy. There may be times when an indirect measure is appropriate, readily available and ethical. Just as with all measurement, however, you should be sure to attempt to estimate the reliability and validity of the measures. For instance, collecting radio station preferences at two different time periods and correlating the results might be useful for

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assessing test-retest reliability. Or, you can include the indirect measure along with other direct measures of the same construct (perhaps in a pilot study) to help establish construct validity. 2. Content Analysis Content analysis is the analysis of text documents. The analysis can be quantitative, qualitative or both. Typically, the major purpose of content analysis is to identify patterns in text. Content analysis is an extremely broad area of research. It includes: Thematic analysis of text The identification of themes or major ideas in a document or set of documents. The documents can be any kind of text including field notes, newspaper articles, technical papers or organizational memos. Indexing There are a wide variety of automated methods for rapidly indexing text documents. For instance, Key Words in Context (KWIC) analysis is a computer analysis of text data. A computer program scans the text and indexes all key words. A key word is any term in the text that is not included in an exception dictionary. Typically you would set up an exception dictionary that includes all non-essential words like "is", "and", and "of". All key words are alphabetized and are listed with the text that precedes and follows it so the researcher can see the word in the context in which it occurred in the text. In an analysis of interview text, for instance, one could easily identify all uses of the term "abuse" and the context in which they were used. Quantitative descriptive analysis Here the purpose is to describe features of the text quantitatively. For instance, you might want to find out which words or phrases were used most frequently in the text. Again, this type of analysis is most often done directly with computer programs. Content analysis has several problems you should keep in mind. First, you are limited to the types of information available in text form. If you are studying the way a news story is being handled by the news media, you probably would have a ready population of news stories from which you could sample. However, if you are interested in studying people's views on capital punishment, you are less likely to find an archive of text documents that would be appropriate. Second, you have to be especially careful with sampling in order to avoid bias. For instance, a study of current research on methods of treatment for cancer might use the published literature as the population. This would leave out both the writing on cancer that did not get published for one reason or another as well as the most recent work that has not yet been published. Finally, you have to be careful about interpreting results of automated context analyses. A computer program cannot

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determine what someone meant by a term or phrase. It is relatively easy in a large analysis to misinterpret a result because you did not take into account the subtleties of meaning. However, content analysis has the advantage of being unobtrusive and, depending on whether automated methods exist, can be a relatively rapid method for analyzing large amounts of text. 3. Secondary Analysis of Data Secondary analysis, like content analysis, makes use of already existing sources of data. However, secondary analysis typically refers to the re-analysis of quantitative data rather than text. In our modern world there is an unbelievable mass of data that is routinely collected by governments, businesses, schools, and other organizations. Much of this information is stored in electronic databases that can be accessed and analyzed. In addition, many research projects store their raw data in electronic form in computer archives so that others can also analyze the data. Among the data available for secondary analysis is:     

census bureau data crime records standardized testing data economic data consumer data

Secondary analysis often involves combining information from multiple databases to examine research questions. For example, you might join crime data with census information to assess patterns in criminal behavior by geographic location and group. Secondary analysis has several advantages. First, it is efficient. It makes use of data that were already collected by someone else. It is the research equivalent of recycling. Second, it often allows you to extend the scope of your study considerably. In many small research projects it is impossible to consider taking a national sample because of the costs involved. Many archived databases are already national in scope and, by using them, you can leverage a relatively small budget into a much broader study than if you collected the data yourself. However, secondary analysis is not without difficulties. Frequently it is no trivial matter to access and link data from large complex databases. Often the researcher has to make assumptions about what data to combine and which variables are appropriately aggregated into indexes. Perhaps more importantly, when you use data collected by others you often don't know what problems occurred in the original data collection. Large, well-financed national studies are usually documented quite thoroughly, but even detailed documentation of procedures is often no substitute for direct experience collecting data.

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One of the most important and least utilized purposes of secondary analysis i s to replicate prior research findings. In any original data analysis there is the potential for errors. In addition, each data analyst tends to approach the analysis from their own perspective using analytic tools they are familiar with. In most research the data are analyzed only once by the original research team. It seems an awful waste. Data that might have taken months or years to collect is only examined once in a relatively brief way and from one analyst's perspective. In social research we generally do a terrible job of documenting and archiving the data from individual studies and making these available in electronic form for others to re-analyze. And, we tend to give little professional credit to studies that are re-analyses. Nevertheless, in the hard sciences the tradition of replicability of results is a critical one and we in the applied social sciences could benefit by directing more of our efforts to secondary analysis of existing data.

DATA ANALYSIS/STATISTICAL TREATMENT 

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This provides a description of the statistical tools used in the treatment of the data. The discussion follows the order in the specific statement of the problem. The data analysis plan may form a part of the discussion in this section.

Steps to Hypothesis Testing 

Hypothesis testing is used to establish whether the differences exhibited by random samples can be inferred to the populations from which the samples originated. General Assumptions    

Population is normally distributed Random sampling Mutually exclusive comparison samples Data characteristics match statistical technique For interval  / ratio data use



t-tests, Pearson correlation, ANOVA, regression For nominal / ordinal data use



Difference of proportions, chi square and related measures of association 1. State the Hypothesis Null Hypothesis (Ho): There is no difference between ___ and ___.  Alternative Hypothesis (Ha): There is a difference between __ and __.

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Note: The alternative hypothesis will indicate whether a 1-tailed or a 2-tailed test is utilized to reject the null hypothesis. Ha for 1-tail tested: The __ of __ is greater (or less) than the __ of __. 2. Set the Rejection Criteria This determines how different the parameters and/or statistics must be before the null hypothesis can be rejected. This "region of rejection" is based on alpha ( ) -the error associated with the confidence level. The point of rejection is known as the critical value. 3. Compute the Test Statistic The collected data are converted into standardized scores for comparison with the critical value. 4. Decide Results of Null Hypothesis If the test statistic equals or exceeds the region of rejection bracketed by the critical value(s), the null hypothesis is rejected. In other words, the chance that the difference exhibited between the sample statistics is due to sampling error is remote--there is an actual difference in the population.

SUMMARY OF THE MEASURES OF CORRELATION (According to Measurement Scale of the Variables) Classification of the Paired Variables X (Independent Variable) Y (Dependent Variable)

Measure of Correlation

Interval/Ratio

Interval/Ratio

Pearson r

Ordinal (Ranked Data)

Ordinal (Ranked Data)

Spearman’s rho

Categorical (2 categories)

Interval

Categorical (3 categories)

Interval

Categorical

Categorical

Categorical

Categorical

Point-Biserial (Pearson’s r can be used provided the categories are coded 1 and 0) ETA Correlation Phi Coefficient (for 2 x 2) Cramer’s V Contingency Coefficient (Chi-square) Lambda Coefficient

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(Unordered)

(Unordered)

Categorical (Ordered)

Categorical (Ordered)

Gamma Coefficient

SUMMARY OF THE MEASURES OF DIFFERENCE (According to Measurement Scale of the Variables) Data Type

No. of Groups 2

Ratio or Interval 3 or more

2 Ordinal (Ranks) 3 or more

Nominal (Frequency Counts)

2 3 or more

Independent t-test for Independent Samples One-Way Analysis of Variance Wilcoxon RankSum Test (MannWhitney U-Test) Kruskal-Wallis OneWay ANOVA

Dependent t-test for dependent samples

Pearson ChiSquare Test Pearson ChiSquare Test

MacNemar’s Test

Repeated Measures ANOVA Wilcoxon Signed Ranked Test Friedman’s TwoWay ANOVA

Cochran’s Q Test

THE BIBLIOGRAPHY/REFERENCES

This portion of thesis or dissertation is composed of all the works consulted for the purpose of the study. This includes books, unpublished thesis / dissertation, journals/ periodicals, and public documents. (Please see a separate Handout on DOCUMENTATION STYLE)

THE APPENDICES

These include the materials which are considered significance in a thesis/ dissertation. Usually, appendices are give titles.